Intensive Care Utilization during Hospital Admission for Delivery

2000 ◽  
Vol 92 (6) ◽  
pp. 1537-1544 ◽  
Author(s):  
Sumedha Panchal ◽  
Amelia M. Arria ◽  
Andrew P. Harris
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Hospital Admission ◽  
Hospital Setting ◽  
Mortality Rates ◽  
Cerebrovascular Event ◽  
Icu Mortality ◽  
Factors Associated ◽  
Icu Admission ◽  
Common Risk Factors

Background During childbirth, the maternal need for intensive care unit (ICU) services is not well-defined. This information could influence the decision whether to incorporate ICU services into the labor and delivery suite. Methods This study reports (1) ICU use and mortality rates in a statewide population of obstetric patients during their hospital admission for childbirth, and (2) the risk factors associated with ICU admission and mortality. A case-control design using patient records from a state-maintained anonymous database for the years 1984-1997 was used. Outcome variables included ICU use and mortality rates. Results Of the 822,591 hospital admissions for delivery of neonates during the study period, there were 1,023 ICU admissions (0.12%) and 34 ICU deaths (3.3%). Age, race, hospital type, volume of deliveries, and source of admission independently and in combination were associated with ICU admission (P < 0.05). The most common risk factors associated with ICU admission included cesarean section, preeclampsia or eclampsia, and postpartum hemorrhage (P < 0.001). Black race, high hospital volume of deliveries, and longer duration of ICU stay were associated with ICU mortality (P < 0.05). The most common risk factors associated with ICU mortality included pulmonary complications, shock, cerebrovascular event, and drug dependence (P < 0.05). Conclusions This study shows that ICU use and mortality rate during hospital admission for delivery of a neonate is low. These results may influence the location of perinatal ICU services in the hospital setting.

Risk Factors Associated With Resistance to Ciprofloxacin in Clinical Bacterial Isolates From Intensive Care Unit Patients

2007 ◽  
Vol 28 (3) ◽  
pp. 331-336 ◽  
Author(s):  
Phillip D. Levin ◽  
Robert A. Fowler ◽  
Cameron Guest ◽  
William J. Sibbald ◽  
Alex Kiss ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prior Treatment ◽  
Bacterial Isolates ◽  
Icu Patients ◽  
Gram Negative ◽  
Factors Associated ◽  
Icu Admission ◽  
Ciprofloxacin Resistance

Objective.To determine risk factors and outcomes associated with ciprofloxacin resistance in clinical bacterial isolates from intensive care unit (ICU) patients.Design.Prospective cohort study.Setting.Twenty-bed medical-surgical ICU in a Canadian tertiary care teaching hospital.Patients.All patients admitted to the ICU with a stay of at least 72 hours between January 1 and December 31, 2003.Methods.Prospective surveillance to determine patient comorbidities, use of medical devices, nosocomial infections, use of antimicrobials, and outcomes. Characteristics of patients with a ciprofloxacin-resistant gram-negative bacterial organism were compared with characteristics of patients without these pathogens.Results.Ciprofloxacin-resistant organisms were recovered from 20 (6%) of 338 ICU patients, representing 38 (21%) of 178 nonduplicate isolates of gram-negative bacilli. Forty-nine percent ofPseudomonas aeruginosaisolates and 29% ofEscherichia coliisolates were resistant to ciprofloxacin. In a multivariate analysis, independent risk factors associated with the recovery of a ciprofloxacin-resistant organism included duration of prior treatment with ciprofloxacin (relative risk [RR], 1.15 per day [95% confidence interval {CI}, 1.08-1.23];P< .001), duration of prior treatment with levofloxacin (RR, 1.39 per day [95% CI, 1.01-1.91];P= .04), and length of hospital stay prior to ICU admission (RR, 1.02 per day [95% CI, 1.01-1.03];P= .005). Neither ICU mortality (15% of patients with a ciprofloxacin-resistant isolate vs 23% of patients with a ciprofloxacin-susceptible isolate;P= .58 ) nor in-hospital mortality (30% vs 34%;P= .81 ) were statistically significantly associated with ciprofloxacin resistance.Conclusions.ICU patients are at risk of developing infections due to ciprofloxacin-resistant organisms. Variables associated with ciprofloxacin resistance include prior use of fluoroquinolones and duration of hospitalization prior to ICU admission. Recognition of these risk factors may influence antibiotic treatment decisions.

scholarly journals The swedish covid‐19 intensive care cohort: Risk factors of ICU admission and ICU mortality

2021 ◽  
Author(s):  
Björn Ahlström ◽  
Robert Frithiof ◽  
Michael Hultström ◽  
Ing‐Marie Larsson ◽  
Gunnar Strandberg ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Icu Mortality ◽  
Icu Admission

scholarly journals Factors Associated with 90-Day Mortality in Invasively Ventilated Patients with COVID-19 in Marseille, France

2021 ◽  
Vol 10 (23) ◽  
pp. 5650
Author(s):  
Maxime Volff ◽  
David Tonon ◽  
Youri Bommel ◽  
Noémie Peres ◽  
David Lagier ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Intensive Care Units ◽  
Arterial Thrombosis ◽  
Invasive Mechanical Ventilation ◽  
Multivariable Analysis ◽  
Factors Associated ◽  
Icu Admission ◽  
Ventilated Patients

Objectives: To describe clinical characteristics and management of intensive care units (ICU) patients with laboratory-confirmed COVID-19 and to determine 90-day mortality after ICU admission and associated risk factors. Methods: This observational retrospective study was conducted in six intensive care units (ICUs) in three university hospitals in Marseille, France. Between 10 March and 10 May 2020, all adult patients admitted in ICU with laboratory-confirmed SARS-CoV-2 and respiratory failure were eligible for inclusion. The statistical analysis was focused on the mechanically ventilated patients. The primary outcome was the 90-day mortality after ICU admission. Results: Included in the study were 172 patients with COVID-19 related respiratory failure, 117 of whom (67%) received invasive mechanical ventilation. 90-day mortality of the invasively ventilated patients was 27.4%. Median duration of ventilation and median length of stay in ICU for these patients were 20 (9–33) days and 29 (17–46) days. Mortality increased with the severity of ARDS at ICU admission. After multivariable analysis was carried out, risk factors associated with 90-day mortality were age, elevated Charlson comorbidity index, chronic statins intake and occurrence of an arterial thrombosis. Conclusion: In this cohort, age and number of comorbidities were the main predictors of mortality in invasively ventilated patients. The only modifiable factor associated with mortality in multivariate analysis was arterial thrombosis.

scholarly journals Risk Factors Associated with Vancomycin-ResistantEnterococcusin Intensive Care Unit Settings in Saudi Arabia

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Mahmoud Shorman ◽  
Jaffar A. Al-Tawfiq
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Saudi Arabia ◽  
Intensive Care ◽  
Antimicrobial Agents ◽  
Multiorgan Failure ◽  
Tertiary Hospital ◽  
Oral Contrast ◽  
Factors Associated ◽  
Icu Admission

Background. Vancomycin-resistant enterococci (VRE) are significant nosocomial pathogens worldwide. There is one report about the epidemiology of VRE in Saudi Arabia.Objective. To determine the risk factors associated with VRE infection or colonization in intensive care unit (ICU) settings.Design. This is a descriptive, epidemiologic hospital-based case-control study of patients with VRE from February 2006 to March 2010 in ICU in a tertiary hospital in Saudi Arabia.Methods. Data were collected from hospital records of patients with VRE. The main outcome measure was the adjusted odds ratio estimates of potential risk factors for VRE.Results. Factors associated with VRE included ICU admission for multiorgan failure, chronic renal failure, prior use of antimicrobial agents in the past three months and before ICU admission, gastrointestinal oral contrast procedure, and hemodialysis. Being located in a high risk room (roommate of patients colonized or infected with VRE) was found to be protective.Conclusions. Factors associated with VRE acquisition are often complex and may be confounded by local variables.

scholarly journals A Systematic Literature Review and Meta-Analysis of Factors Associated with Methicillin-ResistantStaphylococcus aureusColonization at Time of Hospital or Intensive Care Unit Admission

2013 ◽  
Vol 34 (10) ◽  
pp. 1077-1086 ◽  
Author(s):  
James A. McKinnell ◽  
Loren G. Miller ◽  
Samantha J. Eells ◽  
Eric Cui ◽  
Susan S. Huang
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
High Risk ◽  
Hospital Admission ◽  
Systematic Evaluation ◽  
Factors Associated ◽  
Icu Admission ◽  
Increased Risk ◽  
Healthcare Associated ◽  
Mrsa Colonization

Objective.Screening for methicillin-resistantStaphylococcus aureus(MRSA) in high-risk patients is a legislative mandate in 9 US states and has been adopted by many hospitals. Definitions of high risk differ among hospitals and state laws. A systematic evaluation of factors associated with colonization is lacking. We performed a systematic review of the literature to assess factors associated with MRSA colonization at hospital admission.Design.We searched MEDLINE from 1966 to 2012 for articles comparing MRSA colonized and noncolonized patients on hospital or intensive care unit (ICU) admission. Data were extracted using a standardized instrument. Meta-analyses were performed to identify factors associated with MRSA colonization.Results.We reviewed 4,381 abstracts; 29 articles met inclusion criteria (n= 76,913 patients). MRSA colonization at hospital admission was associated with recent prior hospitalization (odds ratio [OR], 2.4 [95% confidence interval (CI), 1.3–4.7];P<.01), nursing home exposure (OR, 3.8 [95% CI, 2.3–6.3];P< .01), and history of exposure to healthcare-associated pathogens (MRSA carriage: OR, 8.0 [95% CI, 4.2–15.1];Clostridium difficileinfection: OR, 3.4 [95% CI, 2.2–5.3]; vancomycin-resistantEnterococcicarriage: OR, 3.1 [95% CI, 2.5–4.0];P< .01 for all). Select comorbidities were associated with MRSA colonization (congestive heart failure, diabetes, pulmonary disease, immunosuppression, and renal failure;P< .01 for all), while others were not (human immunodeficiency virus, cirrhosis, and malignancy). ICU admission was not associated with an increased risk of MRSA colonization (OR, 1.1 [95% CI, 0.6–1.8];P= .87).Conclusions.MRSA colonization on hospital admission was associated with healthcare contact, previous healthcare-associated pathogens, and select comorbid conditions. ICU admission was not associated with MRSA colonization, although this is commonly used in state mandates for MRSA screening. Infection prevention programs utilizing targeted MRSA screening may consider our results to define patients likely to have MRSA colonization.

scholarly journals Outcomes and Prognostic Factors in Patients with Hematologic Malignancies in the Intensive Care unit: A Single-Center Cohort Study of 233 Cases in Taiwan

2021 ◽  
Author(s):  
Chieh-Lung Chen ◽  
Sing-Ting Wang ◽  
Wen-Chien Cheng ◽  
Chih-Yu Chen ◽  
Wei-Cheng Chen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Multivariate Analysis ◽  
Intensive Care ◽  
Renal Replacement Therapy ◽  
Sofa Score ◽  
Critically Ill Patients ◽  
Hematologic Malignancies ◽  
Mortality Rates ◽  
Icu Mortality ◽  
Icu Admission

Abstract BackgroundPatients with a hematologic malignancies (HM) have one of the highest mortality rates among cancer patients admitted to the medical intensive care unit (ICU). The aim of this study was to identify outcomes and risk factors that predict the prognosis of critically ill patients with HM in the ICU.MethodsA retrospective observational study was conducted in a tertiary referral hospital in Taiwan over 40 months (January 1, 2017–April 30, 2020). All adult patients with HM who were admitted to medical ICU were enrolled. Clinical data upon hospital and ICU admission were collected. The predictors of ICU mortality were evaluated using a multivariate analysis.ResultsA total of 233 patients with HM met the inclusion criteria. The median age (SD) was 59.3 (15.1) years, and 76% of the HMs were classified as high-grade disease. The median (IQR) Sequential Organ Failure Assessment (SOFA) score at ICU admission was 11 (9–15); Simplified Acute Physiology Score II, 64 (51–80); and Acute Physiology and Chronic Health Evaluation II score, 28 (23–34). The most common reasons for ICU admission were acute respiratory failure (63.1%) and septic shock (19.7%). The ICU and hospital mortality rates were 54.1% and 67.8%, respectively. A multivariate analysis revealed that the initiation of renal replacement therapy in the ICU (odds ratio [OR], 3.88; 95% CI, 1.66–9.08) and SOFA score (OR, 1.16; 95% CI, 1.03–1.31) were independently associated with ICU mortality.ConclusionsThe ICU and hospital outcomes of critically ill patients with HM are improving. Performance status, cancer status, invasive mechanical ventilation, severe neutropenia, and transplantation status were not identified as predictive factors of ICU outcome. Initiation of renal replacement therapy in the ICU and the SOFA score upon ICU admission were independently associated with ICU mortality. We suggest early and timely ICU admission of patients at risk of multiorgan failure.

scholarly journals Deploying unsupervised clustering analysis to derive clinical phenotypes and risk factors associated with mortality risk in 2,022 critically ill patients with COVID-19 in Spain

2021 ◽  
Author(s):  
Alejandro Rodríguez ◽  
Manuel Ruiz Botella ◽  
Ignacio Matín-Loeches ◽  
María Jiménez Herrera ◽  
Jordi Solé-Violan ◽  
...  
Keyword(s):  
Risk Factors ◽  
Intensive Care ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Clustering Analysis ◽  
Older Age ◽  
Icu Mortality ◽  
Factors Associated ◽  
Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to utilize an unsupervised clustering analysis to derive clinical COVID-19 phenotypes and to analyze patient’s factors associated with mortality risk. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the A(mild) phenotype (537;26.7%) included older age (<65 years), fewer abnormal laboratory values and less development of complications, B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. The C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. Crude ICU mortality was 20.3%, 25% and 45.4% for A, B and C phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented machine learning model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice.

scholarly journals Deploying Machine Learning to Validate Clinical Phenotypes and Factors Associated with Mortality risk in 2,022 Critically Ill Patients with COVID-19 in Spain

2020 ◽  
Author(s):  
Alejandro Rodríguez ◽  
Manuel Ruiz Botella ◽  
Ignacio Matín-Loeches ◽  
María Jiménez Herrera ◽  
Jordi Solé-Violan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care ◽  
Critically Ill ◽  
Mortality Risk ◽  
Critically Ill Patients ◽  
Older Age ◽  
Icu Mortality ◽  
Factors Associated ◽  
Clinical Phenotypes

Abstract Background: The identification of factors associated with Intensive Care Unit (ICU) mortality and derived clinical phenotypes in COVID-19 patients could help for a more tailored approach to clinical decision-making that improves prognostic outcomes. Methods: Prospective, multicenter, observational study of critically ill patients with confirmed COVID-19 disease and acute respiratory failure admitted from 63 Intensive Care Units(ICU) in Spain. The objective was to analyze patient’s factors associated with mortality risk and utilize a Machine Learning(ML) to derive clinical COVID-19 phenotypes. Patient features including demographics and clinical data at ICU admission were analyzed. Generalized linear models were used to determine ICU morality risk factors. An unsupervised clustering analysis was applied to determine presence of phenotypes. The prognostic models were validated and their performance was measured using accuracy test, sensitivity, specificity and ROC curves. Results: The database included a total of 2,022 patients (mean age 64[IQR5-71] years, 1423(70.4%) male, median APACHE II score (13[IQR10-17]) and SOFA score (5[IQR3-7]) points. The ICU mortality rate was 32.6%. Of the 3 derived phenotypes, the C(severe) phenotype was the most common (857;42.5%) and was characterized by the interplay of older age (>65 years), high severity of illness and a higher likelihood of development shock. The A(mild) phenotype (537;26.7%) included older age (>65 years), fewer abnormal laboratory values and less development of complications and B (moderate) phenotype (623,30.8%) had similar characteristics of A phenotype but were more likely to present shock. Crude ICU mortality was 45.4%, 25.0% and 20.3% for the C, B and A phenotype respectively. The ICU mortality risk factors and model performance differed between whole population and phenotype classifications.Conclusion: The presented ML model identified three clinical phenotypes that significantly correlated with host-response patterns and ICU mortality. Different risk factors across the whole population and clinical phenotypes were observed which may limit the application of a “one-size-fits-all” model in practice. Funding: None

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